In vivo continuous glucose monitoring using a chip based near infrared sensor

被引:1
作者
Ben Mohammadi, L. [1 ]
Sigloch, S. [1 ]
Frese, I. [1 ]
Welzel, K. [1 ]
Goeddel, M. [1 ]
Klotzbuecher, T. [1 ]
机构
[1] Fraunhofer ICT IMM, D-55129 Mainz, Germany
来源
BIOPHOTONICS: PHOTONIC SOLUTIONS FOR BETTER HEALTH CARE IV | 2014年 / 9129卷
关键词
continuous glucose monitoring; near infrared difference spectroscopy; microdialysis; diabetes; QUANTITATIVE MICRODIALYSIS; WATER;
D O I
10.1117/12.2052216
中图分类号
Q6 [生物物理学];
学科分类号
071011 ;
摘要
Diabetes is a serious health condition considered to be one of the major healthcare epidemics of modern era. An effective treatment of this disease can be only achieved by reliable continuous information on blood glucose levels. In this work we present a minimally invasive, chip-based near infrared (NIR) sensor, combined with microdialysis, for continuous glucose monitoring (CGM). The sensor principle is based on difference absorption spectroscopy in the 1st overtone band of the near infrared spectrum. The device features a multi-emitter LED and InGaAs-Photodiodes, which are located on a single electronic board (non-disposable part), connected to a personal computer via Bluetooth. The disposable part consists of a chip containing the fluidic connections for microdialysis, two fluidic channels acting as optical transmission cells and total internally reflecting mirrors for in-and out-coupling of the LED light to the chip and to the detectors. The sensor is combined with an intraveneous microdialysis to separate the glucose from the cells and proteins in the blood and operates without any chemical consumption. In vitro measurements showed a linear relationship between glucose concentration and the integrated difference signal with a coefficient of determination of 99 % in the relevant physiological concentration range from 0 to 400 mg/dl. In vivo measurements on 10 patients showed that the NIR-CGM sensor data reflects the blood reference values adequately, if a proper calibration and signal drift compensation is applied. The MARE (mean absolute relative error) value taken over all patient data is 13.8 %. The best achieved MARE value is at 4.8 %, whereas the worst is 25.8 %, with a standard deviation of 5.5 %.
引用
收藏
页数:9
相关论文
共 50 条
[31]   Should all patients on insulin be using continuous glucose monitoring? [J].
Distiller, Larry A. .
JOURNAL OF ENDOCRINOLOGY METABOLISM AND DIABETES OF SOUTH AFRICA, 2018, 23 (03) :59-63
[32]   Identifying Continuous Glucose Monitoring Data Using Machine Learning [J].
Herrero, Pau ;
Reddy, Monika ;
Georgiou, Pantelis ;
Oliver, Nick S. .
DIABETES TECHNOLOGY & THERAPEUTICS, 2022, 24 (06) :403-408
[33]   A MEMS differential viscometric sensor for affinity glucose detection in continuous glucose monitoring [J].
Huang, Xian ;
Li, Siqi ;
Davis, Erin ;
Leduc, Charles ;
Ravussin, Yann ;
Cai, Haogang ;
Song, Bing ;
Li, Dachao ;
Accili, Domenico ;
Leibel, Rudolph ;
Wang, Qian ;
Lin, Qiao .
JOURNAL OF MICROMECHANICS AND MICROENGINEERING, 2013, 23 (05)
[34]   In vitro long-term performance study of a near-infrared fluorescence affinity sensor for glucose monitoring [J].
Ballerstadt, R ;
Polak, A ;
Beuhler, A ;
Frye, J .
BIOSENSORS & BIOELECTRONICS, 2004, 19 (08) :905-914
[35]   Continuous glucose monitoring on the ICU using a subcutaneous sensor; [Kontinuierliches Glukosemonitoring auf Intensivstation mit subkutanem Sensor] [J].
Punke M.A. ;
Decker C. ;
Wodack K. ;
Reuter D.A. ;
Kluge S. .
Medizinische Klinik - Intensivmedizin und Notfallmedizin, 2015, 110 (5) :360-363
[36]   Microstrip Line-Based Glucose Sensor for Noninvasive Continuous Monitoring Using the Main Field for Sensing and Multivariable Crosschecking [J].
Huang, Shao Ying ;
Omkar ;
Yoshida, Yu ;
Inda, Adan Jafet Garcia ;
Xavier, Chia Xujie ;
Mu, Wen Chuan ;
Meng, Yu Song ;
Yu, Wenwei .
IEEE SENSORS JOURNAL, 2019, 19 (02) :535-547
[37]   Miniaturization of an Osmotic Pressure-Based Glucose Sensor for Continuous Intraperitoneal and Subcutaneous Glucose Monitoring by Means of Nanotechnology [J].
Pfuetzner, Andreas ;
Tencer, Barbora ;
Stamm, Boris ;
Mehta, Mandar ;
Sharma, Preeti ;
Gilyazev, Rustam ;
Jensch, Hendrick ;
Thome, Nicole ;
Huth, Michael .
SENSORS, 2023, 23 (09)
[38]   Non-Invasive Blood Glucose Monitoring using Near-Infrared Spectroscopy Based on Internet of Things using Machine Learning [J].
Manurung, Betty Elisabeth ;
Munggaran, Hugi Reyhandani ;
Ramadhan, Galih Fajar ;
Koesoema, Allya Paramita .
PROCEEDINGS OF 2019 IEEE R10 HUMANITARIAN TECHNOLOGY CONFERENCE (IEEE R10 HTC 2019), 2019, :5-11
[39]   Continuous Glucose Monitoring by Real Time Sensor in Interstitial Fluid [J].
Honsova, Sarka ;
Navratil, Tomas .
XXXIII MODERNI ELEKTROCHEMICKE METODY, 2013, :63-67
[40]   Ultra-miniaturization of a planar amperometric sensor targeting continuous intradermal glucose monitoring [J].
Ribet, Federico ;
Stemme, Goran ;
Roxhed, Niclas .
BIOSENSORS & BIOELECTRONICS, 2017, 90 :577-583